Combining diverse neural nets

An appropriate use of neural computing techniques is to apply them to problems such as condition monitoring, fault diagnosis, control and sensing, where conventional solutions can be hard to obtain. However, when neural computing techniques are used, it is important that they are employed so as to maximise their performance, and improve their reliability. Their performance is typically assessed in terms of their ability to generalise to a previously unseen test set, although unless the training set is very carefully chosen, 100% accuracy is rarely achieved. Improved performance can result when sets of neural nets are combined in ensembles and ensembles can be viewed as an example of the reliability through redundancy approach that is recommended for conventional software and hardware in safety-critical or safety-related applications. Although there has been recent interest in the use of neural net ensembles, such techniques have yet to be applied to the tasks of condition monitoring and fault diagnosis. In this paper, we focus on the benefits of techniques which promote diversity amongst the members of an ensemble, such that there is a minimum number of coincident failures. The concept of ensemble diversity is considered in some detail, and a hierarchy of four levels of diversity is presented. This hierarchy is then used in the description of the application of ensemble-based techniques to the case study of fault diagnosis of a diesel engine

Dependable, intelligent voting for real-time control software

An intelligent and dependable voting mechanism for use in real-time control applications is presented. Strategies proposed by current safety standards advocate N-version software to minimize the effects of undetected software design faults (bugs). This requires diversity in design but presents a problem in that truly diverse code produces diverse results; that is, differences in output values, timeliness and reliability. Reaching a consensus requires an intelligent voter, especially when non-stop operation is demanded, e.g. in aerospace applications. This paper, therefore, firstly considers the applicable safety standards and the requirements for an intelligent voter service. The use of replicated voters to improve reliability is examined and a mechanism to ensure non-stop operation is presented. The formal mathematical analysis used to verify the crucial behavioural properties of the voting service design is detailed. Finally, the use of neural nets and genetic algorithms to create N- version redundant voters, is considered

Towards a Comparative Study of Neural Networks in Inverse Model Learning and Compensation Applied to Dynamic Robot Control

This report deals with the applications of neural networks in inverse model learning and compensation to the mobile manipulator dynamic trajectory tracking and control. The mobile base is subject to a non-holonomic constraint and the base and onboard manipulator case disturbances to each other. Compensational neural network controllers are proposed to learn to reach a sequence of targets with given times, to track dynamic trajectories under a non-holonomic constraint and torque limit constraint and to compensate for uncertainties in the non-holonomic base and the manipulator and the disturbances between the base and the manipulator. Both multi-layered perceptron networks and radial basis function networks are considered in the report. Comparison was made between neural network controllers with and without model information. It is shown through various simulations the proposed neural network compensation schemes perform better than conditional controllers

Connectionism And The Issues Of Compositionality And Systematicity

Connectionism as a model of the mind has been attacked by the advocators of the classical paradigm, who claim that Connectionism can only work if it is an implementation of Classical representations. This could be true for some of the models that claim to be Connectionist, but it will in this paper be shown that this is not true for Connectionist architectures that use non-symbolic representations. We will provide evidence in the form of simulation results that severely weaken of the arguments raised by Fodor and Pylyshyn and Fodor and McLaughlin, including their two main arguments, which are the lack of compositionality and systematicity. 1 INTRODUCTION It has been argued that Connectionist models of the mind are mere implementations of Classical models, which are characterised, according to Fodor and Pylyshyn [1], by, 1) Combinatorial syntax and semantics for mental representations... in which (a) there is a distinction between structurally atomic and structurally molecular..

Holistic competence level of Corporate governance bodies in the selection process in the Czech Republic

The main objective of the paper has been based on two-phased research to compare the level of holistic competence in selecting the members of governance bodies (Managing Board and Supervisory Board) between the two periods. Holistic competence was defined based on a holistic model of competence by Porvaznik (2008) and implementation of the model was subsequently described in the Corporate Governance based on a study by Taraba, Bartosikova and Bilikova (2014). Using methods of descriptive statistics and spider charts two data files were compared. Data were collected by questionnaire survey conducted among members of the Corporate Governance bodies operating in the Czech Republic in the period from April 2012 to April 2013 (Phase I) and in the period from September 2013 to February 2014 (Phase II.). There is also discussion as a part of paper conclusion which formulates the underlying causes of changes in the holistic assessment of competence in the selection of the members of corporate governance bodies and the recommendations made in this area

Representation = grounded information

Abstract. The grounding problem remains one of the most fundamental issues in the field of Artificial Intelligence. We argue that representations are grounded information and that an intelligent system should be able to make and manage its own representations. A perusal of the literature reveals much confusion and little progress in understanding the grounding problem. In this paper we delineate between information and representation where a representation is grounded information; as a result we provide much needed clarity and a new base from which to conduct an innovative analysis of grounding that delivers a novel and insightful understanding of intelligence that can be used to guide and inform the design and construction of robust autonomous intelligent systems with intention and that know what they are doing and why they are doing it